Explain Before You Answer: A Survey on Compositional Visual Reasoning
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
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2025
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| _version_ | 1866916920253480960 |
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| author | Ke, Fucai Hsu, Joy Cai, Zhixi Ma, Zixian Zheng, Xin Wu, Xindi Huang, Sukai Wang, Weiqing Haghighi, Pari Delir Haffari, Gholamreza Krishna, Ranjay Wu, Jiajun Rezatofighi, Hamid |
| author_facet | Ke, Fucai Hsu, Joy Cai, Zhixi Ma, Zixian Zheng, Xin Wu, Xindi Huang, Sukai Wang, Weiqing Haghighi, Pari Delir Haffari, Gholamreza Krishna, Ranjay Wu, Jiajun Rezatofighi, Hamid |
| contents | Compositional visual reasoning has emerged as a key research frontier in multimodal AI, aiming to endow machines with the human-like ability to decompose visual scenes, ground intermediate concepts, and perform multi-step logical inference. While early surveys focus on monolithic vision-language models or general multimodal reasoning, a dedicated synthesis of the rapidly expanding compositional visual reasoning literature is still missing. We fill this gap with a comprehensive survey spanning 2023 to 2025 that systematically reviews 260+ papers from top venues (CVPR, ICCV, NeurIPS, ICML, ACL, etc.). We first formalize core definitions and describe why compositional approaches offer advantages in cognitive alignment, semantic fidelity, robustness, interpretability, and data efficiency. Next, we trace a five-stage paradigm shift: from prompt-enhanced language-centric pipelines, through tool-enhanced LLMs and tool-enhanced VLMs, to recently minted chain-of-thought reasoning and unified agentic VLMs, highlighting their architectural designs, strengths, and limitations. We then catalog 60+ benchmarks and corresponding metrics that probe compositional visual reasoning along dimensions such as grounding accuracy, chain-of-thought faithfulness, and high-resolution perception. Drawing on these analyses, we distill key insights, identify open challenges (e.g., limitations of LLM-based reasoning, hallucination, a bias toward deductive reasoning, scalable supervision, tool integration, and benchmark limitations), and outline future directions, including world-model integration, human-AI collaborative reasoning, and richer evaluation protocols. By offering a unified taxonomy, historical roadmap, and critical outlook, this survey aims to serve as a foundational reference and inspire the next generation of compositional visual reasoning research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17298 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explain Before You Answer: A Survey on Compositional Visual Reasoning Ke, Fucai Hsu, Joy Cai, Zhixi Ma, Zixian Zheng, Xin Wu, Xindi Huang, Sukai Wang, Weiqing Haghighi, Pari Delir Haffari, Gholamreza Krishna, Ranjay Wu, Jiajun Rezatofighi, Hamid Computer Vision and Pattern Recognition Artificial Intelligence Compositional visual reasoning has emerged as a key research frontier in multimodal AI, aiming to endow machines with the human-like ability to decompose visual scenes, ground intermediate concepts, and perform multi-step logical inference. While early surveys focus on monolithic vision-language models or general multimodal reasoning, a dedicated synthesis of the rapidly expanding compositional visual reasoning literature is still missing. We fill this gap with a comprehensive survey spanning 2023 to 2025 that systematically reviews 260+ papers from top venues (CVPR, ICCV, NeurIPS, ICML, ACL, etc.). We first formalize core definitions and describe why compositional approaches offer advantages in cognitive alignment, semantic fidelity, robustness, interpretability, and data efficiency. Next, we trace a five-stage paradigm shift: from prompt-enhanced language-centric pipelines, through tool-enhanced LLMs and tool-enhanced VLMs, to recently minted chain-of-thought reasoning and unified agentic VLMs, highlighting their architectural designs, strengths, and limitations. We then catalog 60+ benchmarks and corresponding metrics that probe compositional visual reasoning along dimensions such as grounding accuracy, chain-of-thought faithfulness, and high-resolution perception. Drawing on these analyses, we distill key insights, identify open challenges (e.g., limitations of LLM-based reasoning, hallucination, a bias toward deductive reasoning, scalable supervision, tool integration, and benchmark limitations), and outline future directions, including world-model integration, human-AI collaborative reasoning, and richer evaluation protocols. By offering a unified taxonomy, historical roadmap, and critical outlook, this survey aims to serve as a foundational reference and inspire the next generation of compositional visual reasoning research. |
| title | Explain Before You Answer: A Survey on Compositional Visual Reasoning |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2508.17298 |